Triple
T11870926
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tainan Airport |
E282403
|
entity |
| Predicate | hasCode |
P9567
|
FINISHED |
| Object | RCNN |
E951132
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: RCNN | Statement: [Tainan Airport, hasCode, RCNN]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: RCNN Context triple: [Tainan Airport, hasCode, RCNN]
-
A.
RCNN
chosen
RCNN is the ICAO airport code assigned to Tainan Airport in Tainan, Taiwan.
-
B.
FasterRCNN
FasterRCNN is a popular two-stage object detection architecture that first proposes candidate regions and then classifies and refines bounding boxes, widely used in computer vision tasks.
-
C.
MaskRCNN
MaskRCNN is a deep learning model architecture for instance segmentation that extends Faster R-CNN by adding a branch to predict segmentation masks for individual objects in an image.
-
D.
RetinaNet
RetinaNet is a deep learning–based one-stage object detection model known for its focal loss function, which effectively addresses class imbalance to achieve high accuracy and speed.
-
E.
YOLO
"YOLO" is a comedic hip-hop song and music video by The Lonely Island, featuring Adam Levine and Kendrick Lamar, that parodies the phrase "you only live once" by humorously promoting extreme caution and risk avoidance.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d6ab2945d081908a5851c916cbcfb5 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8be15fb2481908f514781ce2c617f |
completed | April 10, 2026, 9:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f417bb131c8190b0923e077cca74be |
completed | May 1, 2026, 3:02 a.m. |
Created at: April 8, 2026, 9:43 p.m.